A Non-Bipartite Matching Framework for Difference-in-Differences with General Treatment Types
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适用于一般处理类型的双重差分非二分匹配框架
- 研究问题
- 面对二元、序数或连续处理，如何构造保留处理剂量信息的双重差分设计，并减少对参数模型和静态处理单位的依赖？
- 方法与思路
- 作者通过最优非二分匹配平衡基线协变量并扩大处理轨迹差异，提出匹配后随机化条件及样本平均双重差分比率。
- 材料陈述的贡献
- 摘要称，该框架提供有限总体中的非参数因果估计对象与设计型推断，无需依赖处理水平不变或近乎不变的单位。
- 阅读关注点
- 应核对匹配目标、匹配后随机化条件的可辩护性，以及比率估计对象的因果解释和处理轨迹对比的作用。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收均未核实；摘要未说明随机化条件的完整形式及有效推断的具体要求。
原文摘要
Difference-in-differences (DID) is one of the most widely used causal inference frameworks in observational studies. However, most existing DID methods are designed for binary treatments and cannot be readily applied to non-binary treatment settings. Although recent work has begun to extend DID to non-binary (e.g., continuous) treatments, these approaches typically require strong additional assumptions, including parametric outcome models or the presence of idealized comparison units with (nearly) static treatment levels over time (commonly called ``stayers'' or ``quasi-stayers''). In this technical note, we introduce a new non-bipartite matching framework for DID that naturally accommodates general treatment types (e.g., binary, ordinal, or continuous). Our framework makes three main contributions. First, we develop an optimal non-bipartite matching design for DID that jointly balances baseline covariates across comparable units (reducing bias) and maximizes contrasts in treatment trajectories over time (improving efficiency). Second, we establish a post-matching randomization condition, the design-based counterpart to the traditional parallel-trends assumption, which enables valid design-based inference. Third, we introduce the sample average DID ratio, a finite-population-valid and fully nonparametric causal estimand applicable to arbitrary treatment types. Our design-based approach that preserves the full treatment-dose information, avoids parametric assumptions, does not rely on the existence of stayers or quasi-stayers, and operates entirely within a finite-population framework, without appealing to hypothetical super-populations or outcome distributions.